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. Author manuscript; available in PMC: 2026 Oct 2.
Published in final edited form as: Immunity. 2025 Oct 2;58(11):2753–2768.e6. doi: 10.1016/j.immuni.2025.09.007

Notch3 destabilizes Regulatory T cells to Drive Autoimmune Neuroinflammation in Multiple Sclerosis

Mehdi Benamar 1,2, Paola Contini 3,4, Klaus Schmitz-Abe 1,2,5, Olga Lanzetta 6, Feven Getachew 1, Corinne Bachelin 7, Juan Manuel Leyva Castillo 1,2, Muyun Wang 1, Fatma Betul Oktelik 1,8, Océane Perrot 7, Yvann Batamack 1, Sena Nur Arbag 1, Emmanuel Stephen-Victor 1,2,9, Hani Harb 1,2,10, Pankaj B Agrawal 5, Céline Louapre 7, Federico Ivaldi 4, Antonio Uccelli 11,12, Matilde Inglese 11,12, Claudia Angelini 13, Violetta Zujovic 7, Raffaele De Palma 3,4,14,†, Talal A Chatila 1,2,†
PMCID: PMC12671433  NIHMSID: NIHMS2112178  PMID: 41043415

Abstract

The immune regulatory defects that promote neuroinflammation in multiple sclerosis (MS) remain unclear. We show that a specific regulatory T (Treg) cell subpopulation expressing Notch3 was increased in individuals with MS and in mice with experimental autoimmune encephalomyelitis (EAE). Notch3+ Treg cells were induced by the gut microbiota via toll-like receptor (TLR)-dependent mechanisms. They then translocated to the central nervous system (CNS) in EAE where they promoted disease severity. Notch3 interacted with delta-like ligand 1 (DLL1) on microglia to subvert Treg cells into T helper 17 (Th17) cells. Notch3 deletion in Treg cells prevented EAE onset by stabilizing Treg cells and by simultaneously promoting the expansion of a tissue-resident Treg cell population that expressed neuropeptide Y receptor 1 (NPY1R) and which suppressed pathogenic IFN-γ+ and GM-CSF+ T cells. Our studies thus identify altered Treg cell population dynamics as a fundamental pathogenic mechanism in autoimmune neuroinflammation.

One sentence Summary:

Autoreactive Notch3+ regulatory T cells direct autoimmune neuroinflammation in Multiple Sclerosis.

Graphical Abstract

Benamar et al demonstrate that Notch3+ Treg cells are increased in multiple sclerosis and in EAE. They are induced in the gut by the microbiota and translocate to the CNS to degenerate into pathogenic Th17 cells. They also outcompete a protective NPY1R+ resident Treg cell population that controls Th1 responses.

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MS is a chronic autoimmune demyelinating disease thought to be triggered by environmental factors in genetically susceptible individuals 1–4. A large body of research on MS patients and mouse models supports a central role for autoreactive CD4+ T helper (Th) subsets producing interferon gamma (IFN-γ), granulocyte-macrophage colony-stimulating factor (GM-CSF), and interleukin-17 (IL-17) in disease pathogenesis 5,6. In studies on EAE, a mouse model of MS, IFNγ was initially considered a major driver of disease. However, IFNγ-deficient mice still develop EAE, albeit with altered pathology, suggesting that IFNγ may have regulatory or context-dependent roles 7–9. In contrast, IL-17 is critically involved in initiating neuroinflammation through modulating the gut microbiota 10–13. IFNγ and IL-17 can have antagonistic or cooperative effects depending on the stage and context of the disease, indicating a complex interplay between Th1 and Th17 responses in CNS autoimmunity 14–17. GM-CSF is primarily produced by activated Th cells, particularly Th17 and Th1 subsets, and is essential for the recruitment, activation, and pathogenic function of myeloid cells such as monocytes and dendritic cells within the CNS 18-20. The critical involvement of GM-CSF in driving CNS inflammation is underscored by studies showing that mice deficient in GM-CSF are resistant to EAE induction 19,20.

Of particular interest in disease pathogenesis is the role of immune regulatory mechanisms normally operative to restrain autoreactivity, key among which are regulatory T (Treg) cells 21. The number and function of Treg cells are impaired in patients with MS, which may contribute to the development and progression of the disease 22-24. In EAE, Treg cells accumulate in the central nervous system (CNS) tissues but fail to control the autoimmune inflammation 25 and eventually lose their suppressive function and Foxp3 expression 26,27. Subverted Treg cells may further convert into pathogenic Th cells under the influence of pro-inflammatory cytokines or antigenic stimulation 24,26. Whether this Treg cell plasticity plays a role in the pathogenesis of EAE and MS remains unclear.

Notch signaling in T cells is implicated in the pathogenesis of MS/EAE 28-32. Our previous studies show that individual Notch receptor signaling in Treg cells licenses inflammation in different tissues by promoting Treg cell destabilization and plasticity. Thus, Notch1 signaling in Treg cells drives Th1-skewing and CD22-dependent gut tissue homing to promote autoimmunity and inflammation 33,34. In contrast, Notch4 is elevated on lung Treg cells to promote their Th2 and Th17 cell skewing in allergic airway inflammation and Th1 skewing in lung viral infections, respectively 35-37. We here identified gut microbiota-induced Notch3+ Treg cells as critical contributors to autoimmune neuroinflammation relevant to MS and EAE. Upon translocating to the CNS, these cells triggered CNS inflammation through interactions with the microglia and concomitant promotion of Th17 cell responses. Furthermore, gut-emigrated Notch3+ Treg cells outcompeted a neuroprotective resident Treg cell population that expressed neuropeptide Y receptor 1 (NPY1R) and controlled IFNγ and GM-CSF responses.

Results:

Treg cells of MS patients have increased Notch3 expression

Analysis of peripheral blood mononuclear cells (PBMCs) from patients with actively relapsing MS revealed an increase in Notch3 expression, but not of other Notch receptors, specifically in Treg cells (Fig. 1A-B; Table S1), but not in CD4+ T conventional (Tconv) (Fig. 1C). Treg cell Notch3 expression was decreased in patients with inactive MS compared to those with active disease and to Healthy controls (Fig. 1D,E). Analysis of cerebrospinal fluid (CSF) T cells of patients with active MS revealed a further increase in Notch3 expression in Treg cells compared to their matched counterparts in the peripheral blood (Fig. 1F). Notch3 expression was also increased but to a lesser extent in Tconv cells in the CSF of MS patients, while Notch3 expression was unchanged in the peripheral blood and CSF T cell subsets of control subjects (Fig. 1G). The frequencies of Tconv cells expressing T helper (Th) cell cytokines implicated in MS pathogenesis, including IL-17+ and IL-17+IFNγ+ 5,38, were increased in the CSF but not PBMCs of MS patients (Fig. 1H-I). Notch3+ Treg cells in the CSF also expressed increased levels of IL-17, IL-22 and IFNγ, indicative of their subversion into a Th cell-like phenotype (Fig. 1JK). These results suggested a putative role for Notch3 in the pathogenesis of MS.

Fig. 1. Increased Notch3 expression on peripheral and CSF Treg cells of MS patients.

Fig. 1.

A-C. Flow cytometric analysis of Notch3 expression in PBMCs Treg cells (A) and frequencies of the respective Notch receptor-expressing CD4+ Treg (Foxp3+) (B) and Foxp3– cells (C) in PBMC of healthy controls and active remitting-relapsing multiple sclerosis subjects. D and E. Flow cytometric analysis (D) and cell frequencies (E) of Notch3 expression in PBMCs CD4+ Treg cells in healthy controls (HC) and in MS subjects with active remitting relapsing disease or in inactive MS. F and G. Flow cytometric analysis and frequencies of Notch3+ Treg (F) and Foxp3– cells (G) in the CSF of active MS subjects vs. non-MS control subjects. H and I. Flow cytometric analysis and frequencies of IL-22, IL17 (H) and IFNγ (I) expression by Foxp3– cells from CSF of MS patients and non-MS control. Numbers in flow plots are percentages. J and K. Flow cytometric analysis and frequencies of IFNγ (J), IL17 and IL22(K) expression by Foxp3+Notch3+ or Foxp3+Notch3– CD4+ T cells from CSF of MS patients. Numbers in flow plots are percentages. Error bars are SEM. Each point denotes an individual. Statistical analysis: one-way ANOVA with Dunnett’s post-hoc analysis (E, H, I) or two-way ANOVA with Sidak post-hoc analysis (B,C, F, G), or student two-tailed t-test (J,K).

Notch3+ Treg cells promote EAE pathogenesis by augmenting CNS Th17 responses

To examine the functional role of Notch3 in MS, we analyzed Notch receptor expression in EAE induced by immunization of mice with a myelin oligodendrocyte glycoprotein peptide (MOG35-55). In agreement with the human data, Notch3, but not other Notch receptors, was also increased in mouse CNS tissue CD4+ T cells including Treg (Foxp3+) and non-Treg (Foxp3–) cells (Fig, 2A,B). Global deletion of Notch3 attenuated EAE disease severity (Fig. 2C,D). Furthermore, conditional deletion of Notch3 in T cells or more specifically in Treg cells using a floxed Notch3 allele (CD4CreNotch3Δ/Δ and Foxp3YFPCreNotch3Δ/Δ, respectively) similarly attenuated EAE disease severity, pointing to Treg cells as the key drivers of Notch3-mediated EAE pathology (Fig. 2C,D). Moreover, phenotyping of different immune tissues in Foxp3YFPCreNotch3Δ/Δ mice failed to demonstrate any spontaneous immune activation or skewing (Fig. S1A-N). In contrast, Treg cell-specific deletion of Notch1, Notch2 or Notch4 had minimal (Notch1) or no effect (Notch2, Notch4) on EAE disease outcome and cytokine production by CNS CD4 cells after EAE induction (Fig. S2A-D). These results confirmed a specific pathogenic role for Notch3+ Treg cells in EAE.

Fig. 2. Notch3+ Treg cell promotes EAE.

Fig. 2.

A. Flow cytometric analysis of Notch receptors on Foxp3+ and Foxp3– CNS CD4+ cells in Foxp3YFPCre mice in EAE. B. Frequencies of the respective Notch receptor-expressing CNS CD4+ Treg (Foxp3+) and Foxp3– cells in EAE. C and D. Time scores (C), cumulative and maximal scores (D) of EAE in mice that were either Notch3-sufficient (Foxp3YFPCre), or with global (Notch3−/−), T cell-specific (CD4creNotch3Δ/Δ) or Treg cell-specific (Foxp3YFPCreNotch3Δ/Δ) Notch3 deletion. E. Sc-RNA seq analysis of CNS CD4+ T cells isolated from MOG33-55-immunized Foxp3YFPCre (n= 2, pool of 6 mice) and Foxp3YFPCreNotch3Δ/Δ (n= 3, pool of 9 mice). Uniform manifold approximation and projection (UMAP) of normalized and harmonized dataset, split by genotype and color-coded by cluster. (F and G) IPA (F) and upstream regulator analysis (G) of differentially expressed genes found in cluster 7 from MOG 33-55-immunized Foxp3YFPcre and Foxp3YFPCreNotch3Δ/Δmice. H-I. Volcano plot (H) and IPA pathway analysis (I) of gene transcripts of Cluster C7 versus Cluster C0 in Foxp3YFPCre group. J-L Flow cytometric analysis (J) and frequencies (K, L) of CNS and lymph nodes (LN) CD4+ T cells expressing the respectively indicated cytokines in Foxp3YFPCre vs. Foxp3YFPCreNotch3Δ/Δ mice in EAE. Panels B-D and J-L represent a pool of four experiments, panels E-I represent a pool of two experiments. Numbers in flow plots are percentages. Each point represents one mouse. Error bars are SEM. Statistics: two-way ANOVA with Sidak post-hoc analysis (B, J-L) and one-way ANOVA with Dunnett’s post-hoc analysis (D).

To further decipher the role of Notch3+ Treg cells in EAE pathogenesis, we carried out single-cell RNA sequencing (scRNA-seq) on CD4+ T cells from the CNS of MOG-immunized Foxp3YFPCre and Foxp3YFPCreNotch3Δ/Δ mice. We first performed a graph-based clustering analysis using Seurat, which uncovered 12 clusters (Fig. 2E and S3C-F). These clusters included a Treg cell cluster (C0) expressing canonical Treg cell genes (e.g. Foxp3, Ikzf2, Il2ra), an effector CD4+ T cell cluster (C1) (Tbx21, Id2, Cxcr6), a Th17 cluster (C3) (Rorc, Ccr6, Il23r), a T follicular helper cell cluster (C4) (Icos, Pdcd1, Batf), and a mixed Treg/Th17 cluster (C7) (Foxp3, Rorc, Maf) (Fig. 2E, S3F). We performed pseudo-bulk differential gene expression analysis in C0 and C3 followed by pathway and upstream regulator analysis. Results revealed that C0 cluster of Foxp3YFPCreNotch3Δ/Δ mice showed increase in a Treg cell pathway (RUNX1 and Foxp3 control the development of Tregs) and decrease in a pathway involved in Treg cells destabilization (PTEN, PI3K/AKT signaling). The C0 cluster of Foxp3YFPCre had increased MyD88, Hippo pathway and TLR signaling (Fig. S3G-I). Pseudo-bulking analysis of cluster C3 also revealed an increase of MyD88 and TLR signaling in the C3 cluster of Foxp3YFPCre (Fig. S3K-L). Cell proportion analysis revealed that only two clusters were significantly different between the two groups: a naïve CD4 cell cluster (C11) and a Treg/Th17 cluster (C7) (FDR<0.05, see Methods). To further decipher differences in the C7 transcriptomic programs between the respective groups, we performed pseudo-bulk differential gene expression analysis followed by pathway and upstream regulator analysis (Fig. 2F, I). Results revealed that the C7 cluster of Foxp3YFPCreNotch3Δ/Δ mice showed decreased pathways associated with Th17 cell differentiation, including Hippo 35,39,40 and STAT3, and increased genes targeted by Foxp3 compared to the C7 cluster of Foxp3YFPCre group. Upstream regulator analysis performed by Ingenuity pathway analysis (IPA, Qiagen) predicted increased activation of several pathways including Notch (recombination signal-binding protein for immunoglobulin kappa J region, RBPJ) and Hippo (yin yang 1, YY1 and tyrosine 3-monooxygenase/tryptophan 5 monooxygenase activation protein gamma, YWHAG) in the C7 cluster of Foxp3YFPCre group (Figure 2G). We further performed pseudo-bulk differential gene expression analysis between clusters C0 and C7 followed by pathway analysis (Fig. 2H and I). Results revealed that cluster C7 presents a decrease in the Treg cell canonical gene Ikzf2 and an increase in gut Treg cell-associated genes including Maf and Ffar2 41,42 (Fig. 2H). Pathway analysis revealed increased pathways associated with Th17 cell differentiation (Th17 activation pathway, IL-6 family signaling, STAT3 pathway) (Fig. 2I). Overall, these results suggest that Notch3 destabilized Treg cells towards a Th17 phenotype (Fig. 2F-I). Consistent with the results of the scRNA-seq study, analysis of IL-17 producing T cells frequencies in EAE revealed decreased IL-17+IFNγ+, IL-17+GM-CSF+, IL-17+IL-22+ subsets in the CNS of mice with Notch3-deficient Treg cells compared to controls (Fig, 2J-L).

Notch3 deficiency suppressed disease independently of MOG35-55 TCR specificity

Investigation into the antigen specificity of CNS Notch3+ T cells revealed that the frequency of MOG35-55 peptide-specific CD4+ T cells, as measured by staining with MOG35-55 peptide-specific I-Ab tetramers, was sharply decreased in the CNS of Foxp3YFPCreNotch3Δ/Δ compared to Foxp3YFPCre mice following EAE induction (Fig. S2E, F). To discriminate the respective roles of MOG35-55 peptide-specific T cell receptors (TCR) versus Notch3 in the pathogenesis of EAE, we examined disease progression in Notch3-sufficient or -deficient mice carrying a MOG35-55 peptide-specific TCR transgene (2D2) 43. Global Notch3 deletion suppressed the incidence of spontaneous EAE in 2D2 transgenic mice (Fig. S2G). Moreover, induction of EAE and IFNγ+ IL17+ producing CNS CD4 cells was abrogated in Rag1−/− mice that received Notch3-deficient compared to Notch3-sufficient 2D2+ T cells followed by immunization with MOG35-55 peptide (Fig. S2H-L).To further decipher the decrease in MOG35-55 peptide-specific CD4+ T cells at peak of disease, we analyzed the expression of the co-stimulation markers CD80 and CD86 and MHC-II in antigen presenting cells (APCs) and also the frequencies of MOG35-55 peptide-specific CD4+ T cells in the draining lymph nodes at day 6 post disease induction, at which time T cell priming in EAE takes place 44. Results revealed that APC maturation and MOG35-55 peptide-specific CD4+ T cell frequencies in the draining lymph nodes were not impacted by Treg cell Notch3 deletion (Fig. S2M-P). Overall, these results suggested that Notch3 deficiency suppressed disease induction separately from its effect on MOG35-55 peptide-specific TCR cell frequencies or T cell priming.

Notch3 is induced by the gut microbiota via Treg cell Toll-like receptors (TLRs).

Analysis of Notch3 expression in Treg and Tconv cells in different tissues at steady state revealed that they were primarily found in the gut and the CNS (Fig. S4A). Flow cytometric analysis of Notch3+ Treg cells revealed decreased Foxp3 mean fluorescence intensity (MFI) and increased expression of CNS homing (CCR5, CD49d, CXCR3) 45-48 and gut homing marker (α4β7) (Fig. 3A-H). CNS CD4+Notch3+ Treg and non-Treg cells from Foxp3YFPCre mice were highly enriched in MOG35-55 peptide-specific tetramer staining after EAE induction (Fig. 3I-J), suggesting that Notch3 may translocate from the gut to the CNS to promote EAE pathogenicity due to their TCR specificity and the expression of CNS homing markers. CNS Notch3+ Treg cells, isolated after EAE induction, exhibited decreased in vitro suppressive function compared to their Notch3– counterparts (Fig. S4B), consistent with a disease promoting function of Notch3 in CNS Treg cells.

Fig. 3. Notch3+ Treg cells are induced by the gut microbiota and translocate to the CNS.

Fig. 3.

A and B. Mean Fluorescence intensity (MFI) of Foxp3 (A) and Notch3 (B) in CNS Notch3+ versus Notch3– Treg cells. C. Frequencies and MFI of Helios in CNS Notch3+ versus Notch3– Treg cell. D. MFI of CD69 and CD44 in CNS Notch3+ versus Notch3– Treg cells. E-H. Frequencies and MFI of CCR5 (E), CD49d (F), CXCR3 (G), α4β7 (H) in CNS Notch3+ versus Notch3– Treg cell. I and J. Flow cytometric analysis and frequencies of CLIP103−117I-Ab or MOG33-55I-Ab tetramer staining Notch3+ vs. Notch3– CNS CD4+ T cells (I) and Foxp3+ Treg cells at day 15 post-EAE induction (J). K, Flow cytometric analysis and cell frequencies of CNS and gut Notch3+ Treg cells in SPF mice, GF mice or GF mice recolonized with FMT. L-N, Time scores (L) and cumulative (M) and maximal scores (N) of EAE in Foxp3YFPCre, Foxp3YFPCreTlr4Δ/Δ, Foxp3YFPCreTlr2Δ/Δ, Foxp3YFPCreTlr5Δ/Δ, Foxp3YFPCreMyd88Δ/Δ mice. O. Cell frequencies of CNS Notch3+ Treg cells in EAE in Foxp3YFPCre, Foxp3YFPCreTlr2Δ/Δ, Foxp3YFPCreTlr5Δ/Δ and Foxp3YFPCreMyd88Δ/Δ mice. P. Cell frequencies of CNS α4β7+ Treg cells in EAE in Foxp3YFPCre, Foxp3YFPCreTlr2Δ/Δ, Foxp3YFPCreTlr5Δ/Δ and Foxp3YFPCreMyd88Δ/Δ mice. Q, Time scores and cumulative scores of EAE in Foxp3YFPCre mice that were either treated with anti-Vla-4 or anti-α4β7 mAb. R, Notch3 expression in CNS Treg in Foxp3YFPCre mice that were either treated with anti-Vla-4 or anti-α4β7 mAb at day 15 after EAE induction. Panels A-H and L-M represent a pool of three experiments, panels I-K represent a pool of two experiments. Error bars are SEM. Each point denotes one mouse. Statistical analysis: student two-tailed t-test (A-H) one-way ANOVA with Dunnett’s post-hoc analysis (I, J, M-P) or two-way ANOVA with Sidak post-hoc analysis (K,R).

Previous studies implicate the gut microbiota and a gut/CNS axis in EAE and in MS 49-55. To analyze the role of the gut microbiota in Notch3 induction, we treated mice with the antibiotic ampicillin, previously reported to suppress EAE induction 52. Ampicillin treatment attenuated EAE in Foxp3YFPCre mice in association with decreased CNS Notch3+ Treg cells, suggesting that Notch3 is induced by a gut-microbiota-dependent mechanism (Fig. S4C, D). Consistent with their gut microbiota-dependent ontogeny, Notch3+ Treg cells were minimally detected in the gut and CNS tissues of germ-free (GF) mice as compared to specific pathogen-free (SPF) mice but were induced upon fecal microbiota transplant of GF mice (Fig. 3K). Further analysis showed that Treg cell-specific deficiency of MyD88, and to a lesser extent the TLR2 and TLR5 but not TLR4, suppressed EAE disease severity and decreased the frequency of IFNγ+IL17+ and IL22+IL17+ CNS T cells (Fig. 3L-N and Fig S4 E and F). Disease attenuation was associated with decreased expression by CNS Treg cells of Notch3 and the gut homing integrin α4β7, consistent with the induction of Notch3+ Treg cells by TLR-activating gut microbial signals (Fig. 3 O and P). To confirm the gut ontogeny of CNS Notch3+ Treg cells in EAE, we transferred naïve CD4+ T cells into Rag1−/− immunodeficient mice which were then immunized with MOG35-55. Notch3+ Treg cells first appeared in the gut lamina propria at day 4 post-immunization and then appeared in the CNS at day 7 post-immunization, suggesting that Notch3+ Treg cells are first induced in the gut lamina propria and then migrate to the CNS following EAE induction (Fig. S5A-I). To confirm the gut to CNS translocation of Notch3+ Treg cells, we treated MOG35-55 immunized Foxp3YFPCre mice with either anti-α4β7 mAb, anti-Vla-4 mAb to block the gut or CNS homing. Results revealed that treatment with anti-α4β7 or anti-Vla-4 mAbs but not with an isotype control mAb led to decreased Notch3+ Treg cell frequencies in the CNS and their reciprocal increase in the gut (Fig. 3Q-R). Altogether, these results confirm that Notch3+ Treg cells are induced in the gut by a gut-microbiota-MyD88 dependent mechanisms. They then migrate to the CNS to promote EAE pathogenesis aided by their MOG35-55 specificity and the expression of CNS homing markers.

Notch3 destabilizes Treg cells by Hippo-pathway-dependent mechanisms

In view of the scRNA-seq data implying the destabilization of Notch3+ Treg cells into Th17-like cells following the migration of the former into the CNS in EAE (Fig. 2E-H), we investigated this destabilization process by performing bulk RNA-seq analysis of CNS Treg cells isolated from Foxp3YFPCre versus Foxp3YFPCreNotch3Δ/Δ mice in EAE. Results revealed increased expression of gene transcripts of several pathways associated with Th cell activation and differentiation including Th17 cells, epigenetic modulation and regulatory T cell lineage commitment (Fig. 4A, B). Notch3+ Treg cell destabilization into Th17 cell subsets was further confirmed by a lineage tracing approach using a Foxp3YFPCre and a Cre-activated RFP reporter inserted into the Rosa26 locus (Foxp3YFPCreR26LSL-RFP) 26,56,57. Treg cell-specific Notch3 deletion prevented Treg destabilization and the generation of ex-Treg cells (CD4+YFP–RFP+) following EAE induction (Fig. 4C). Further analysis revealed that the generation of ex-Treg cells preferentially took place in the CNS (brain, meninges and spinal cord tissues) compared to the spleen and the gut (Fig. 4D). Notch3 expression was elevated in CNS Treg and ex-Treg cells (YFP+RFP+ and YFP–RFP+, respectively) but not in Tconv cells (YFP–RFP–) (Fig. 4E, Fig. S5J). Further analysis of cytokine expression in ex-vivo-isolated CNS Treg and ex-Treg cells of EAE mice revealed that upward of 40% of IL-17-expressing cells localized to these populations (Fig. S5K-O). Furthermore, in vitro co-culture of Notch3+ versus Notch3– CNS Treg cells of EAE mice with microglial cells pulsed with MOG35-55 peptide revealed induction of IL-17-expression in the former but not the latter Treg cells (Fig. S5P-R). In contrast, IFNγ expression was similarly low in both populations. Finally, longitudinal analysis revealed that following peak disease at day 15 and the subsequent decline of Notch3+ Treg cell frequencies, Notch3+IL17+ and Notch3+IL-17+IFNγ+ ex-Treg cells persisted at day 30 post-EAE induction (Fig. S5S-T). These results indicated that Notch3+ Treg cell destabilization is a major mechanism driving the Th17 cell response in EAE.

Fig. 4. Notch3 destabilizes Treg cells to promote EAE.

Fig. 4.

A and B. Volcano plot (A) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (B) of gene transcripts of CNS Notch3-sufficient (Foxp3YFPCre, n= 4, pool of 16 mice) vs. Notch3-deficient (Foxp3YFPCreNotch3Δ/Δ, n= 4, pool of 16 mice) Treg cells. C, Flow cytometric analysis of Treg (YFP+RFP+), ex-Treg (YFP+RFP–) and Tconv cells (YFP–RFP–) in Foxp3YFPCreR26LSL-RFP and Foxp3YFPCreNotch3Δ/ΔR26LSL-RFP mice and frequencies of ex-Treg cells in control (PBS) vs. Foxp3YFPCreR26LSL-RFP and Foxp3YFPCreNotch3Δ/ΔR26LSL-RFP mice at day 15 after EAE induction. D, Frequencies of ex-Treg cells in different tissue in Foxp3YFPCreR26LSL-RFP mice at day 15 after EAE induction. E. Flow cytometric analysis and frequencies of Notch3+ Treg, ex-Treg and Tconv cells in Foxp3EGFPCreR26YFP and Foxp3YFPCreNotch3Δ/ΔR26LSL-RFP mice in control (PBS) vs. EAE mice. F. Flow cytometric analysis and frequencies of Yap1 positive Treg cells in Foxp3YFPCre vs. Foxp3EGFPCreNotch3Δ/Δ Treg cells in EAE. G and H. Time scores (G), cumulative and maximal scores (H) of EAE in Foxp3YFPCre, Foxp3YFPCreNotch3Δ/Δ or Foxp3YFPCreYap1Δ/ΔWwtr1Δ/Δ mice. I. Notch3 expression in Treg and Foxp3– cells in Foxp3YFPCre vs. Foxp3YFPCreYap1Δ/ΔWwtr1Δ/Δ mice in EAE. J, Frequencies of ex-Treg cells in Foxp3YFPCreR26LSL-RFP and Foxp3YFPCreYap1Δ/ΔWwtr1Δ/ΔR26LSL-RFP mice at day 15 after EAE induction. K and L. IFNγ+IL17+, IFNγ–IL-17+(K) and IL-22+IL-17+ (L) CD4+ T cells in the respective mouse strains at day 15 post-EAE. M. Flow cytometric analysis of Foxp3 expression and frequencies of Foxp3+ Treg cells in co-cultures of CNS Treg cells derived from MOG33-55-immunized Foxp3YFPCre, Foxp3YFPCreNotch3Δ/Δ and Foxp3YFPCreYap1Δ/ΔWwtr1Δ/Δ mice and co-cultured with microglial cells of MOG33-55-immunized Foxp3YFPCre mice. Panels C-I and K-M represent a pool of three experiments, panels J represent one experiment. Error bars are SEM. Each point denotes one mouse. Statistical analysis: student two-tailed t-test (F, I, J, K, L) one-way ANOVA with Dunnett’s post-hoc analysis (C, D, H, M) or two-way ANOVA with Sidak post-hoc analysis (E).

To determine the molecular mechanisms involved in Notch3-mediated Treg cell destabilization into Th17 cells, we focused on the role in this process of the Hippo pathway, which was implicated in Treg cell destabilization in previous studies 35,39 and by our scRNA-seq analysis (Fig. 2 F and G ). Notch3 deletion profoundly inhibited the expression of the Hippo pathway effector Yap1 in CNS Treg cells in EAE (Fig. 4F). Consistent with these results, Treg cell-specific deletion of Yap1 and Wwtr1, the latter encoding the Yap1 related effector Taz, suppressed disease severity in EAE to a similar extent as Notch3 deletion (Fig. 4 G, H). Combined Yap1 and Wwtr1 deletion did not affect the frequency of Notch3+ Treg cells, but greatly reduced the frequency of Notch3+ Tconv cells (Fig. 4I). Lineage tracing analysis using Foxp3YFPCreR26LSL-RFP and Foxp3YFPCreYap1Δ/ΔWwtr1Δ/ΔR26LSL-RFP mice demonstrated that Treg cell-specific deletion of Yap1 and Wwtr1 suppressed ex-Treg generation after EAE induction (Fig. 4J). It also reduced the IL-17+ Tconv cell subpopulations (Fig. 4 K-L). The role of the Notch3-Hippo pathway in Treg cell destabilization was also confirmed by co-culturing Notch3-sufficient, Notch3-deficient or Yap1/Taz deficient Treg cells derived from MOG35-55-immunized mice of the respective strains with microglia derived from Notch3-sufficient (Foxp3YFPCre) EAE mice. Notch3-deficient and Yap1/Taz-deficient Treg cells were less destabilized in this co-culture system compared to Notch3-sufficient Treg cells, as indicated by higher expression of Foxp3 in the former cells (Fig. 4M). These results indicated that Notch3+ Treg cells are destabilized by a Hippo pathway dependent mechanism in EAE.

Notch3-DLL1 interaction destabilizes Treg cells and promotes pro-inflammatory microglia.

To elucidate the ligand(s) involved in Notch3+ Treg cell-microglia interactions in EAE, we analyzed microglial expression of the different Delta and Jagged Notch ligands. The Notch delta-like ligand 1 (DLL1) was found selectively increased in microglia in EAE in both WT and Treg cell Notch3-deficient mice compared to non-immunized mice (Fig. 5A). Co-culture of CNS Treg cells with microglia isolated from EAE mice revealed that Notch3-sufficient Treg cells were destabilized, as evidenced by decreased Foxp3 expression. Additionally, this destabilization was reversed by treatment with an anti-DLL1 but not and anti-Jagged (Jag)2-antibody. In contrast, Notch3-deficient Treg cells destabilized to a lesser extent under the same culture conditions while treatment with anti-DLL1 antibody did not improve their stability (Fig. 5B). Reciprocally, Notch3-sufficient Treg cells interacted with the microglia in a DLL1-dependent manner to increase CD86 and suppress CD206 and aryl hydrocarbon receptor (AhR) expression (Fig. S6A-C). These results were confirmed in studies showing that the microglia of Foxp3YFPCreNotch3Δ/Δ mice subjected to EAE showed increased expression of CD206 and AhR, both markers of anti-inflammatory M2-like microglia 58, compared to those of similarly treated Foxp3YFPCre mice (Fig. S6D-F). RNA-seq analysis of microglia isolated from mice in EAE also revealed enrichment of several anti-inflammatory pathways in Foxp3YFPCreNotch3Δ/Δ compared to Foxp3YFPCre compared to mice (Fig. S6G,H), including the LXR/RXR 59, sphingomyelin and Retinol pathways 60,61.

Fig. 5. Treg cell Notch3-microglial DLL1 interaction promotes EAE.

Fig. 5.

A. Flow cytometric analysis of DLL1 expression on microglia and frequencies of Jagged (Jag) and Delta-like (DLL) ligand-expressing microglial cells in CNS of Foxp3YFPCre and Foxp3YFPCreNotch3Δ/Δ mice at steady state or after EAE. B. Flow cytometric analysis of Foxp3 expression and frequencies of Foxp3+ Treg cells in co-cultures of CNS Treg cells from MOG33-55-immunized Foxp3YFPCre and Foxp3YFPCreNotch3Δ/Δ mice and microglia derived from MOG33-55-immunized Foxp3YFPCre mice. The co-cultures were incubated in the presence of isotype control, anti-DLL1 or anti-Jag2 antibodies. C. Time scores, cumulative and maximal scores of EAE in Foxp3YFPCreR26LSL-RFP mice either treated with anti-DLL1 or isotype control mAb. D, Frequencies of ex-Treg cells in Foxp3YFPCreR26LSL-RFP mice treated with an isotype control vs Foxp3YFPCreR26LSL-RFP mice treated with anti-DLL1 mAb at day 15 after EAE induction E. Time scores, cumulative and maximal scores of EAE in Foxp3YFPCre, Foxp3YFPCreNotch3Δ/Δ or Foxp3YFPCreLfng1Δ/Δ mice. F-H. Cell frequencies of IFNγ+IL17-, IFNγ+IL17+, IFNγ–IL-17+, IL-22+IL-17+ and IFNγ+GM-CSF+ CD4+ T cells in the respective mouse strain at day 15 post-EAE. I. Flow cytometric analysis, MFI of Foxp3 expression, and frequencies of Foxp3+ Treg cells in co-cultures of CNS Treg cells of Foxp3YFPCre and Foxp3YFPCreLfngΔ/Δ mice with microglial cells of MOG33-55-immunized Foxp3YFPCre mice. Panels A, B, F, I represent a pool of two experiments, panels C and D represent one experiment. Panels E a pool of three experiments and panels F-H represents a pool of two experiments. Error bars are SEM. Each point denotes one mouse. Statistical analysis: two-way ANOVA with Sidak post-hoc analysis (A and B) or one-way ANOVA with Dunnett’s post-hoc analysis (E) or student two-tailed t-test (C, D,F-I).

To determine the role of Notch3-DLL1 interaction in disease pathogenesis, we treated Foxp3YFPCreR26LSL-RFP mice with anti-DLL1 mAb as previously described 62. Anti-DLL1 treatment attenuated EAE severity (Fig. 5C) and prevented CNS ex-Treg cells generation (Fig. 5D). To go further, we analyzed the impact of Treg cell-specific deletion of the gene encoding the Notch receptor modifier Lunatic Fringe (Lfng), which differentially promotes Notch receptor-delta ligand interactions by glycosylating O-fucose residues on the extracellular EGF repeats of Notch receptors 63. Whereas Treg cell-specific deletion of Lfng did not affect T cell development or activation (Fig. S1A-N), it recapitulated the effects of Notch3 in inhibiting EAE and suppressed various CNS Th17 cell subsets (Fig. 5 E-H). In vitro co-culture studies with microglia from EAE mice also demonstrated improved stability of the Lfng-deficient compared to Foxp3YFPCre Treg cells (Fig. 5I). Collectively, these results indicate that Notch3-DLL1 interaction drives Treg cell destabilization in EAE.

Notch3 deletion suppresses pathogenic IFNγ+GM-CSF+ T cells by promoting a NPY1R+ Treg cell population.

One key gene that emerged from the analysis of RNA-seq data of Foxp3YFPCre compared to Notch3-deficient Treg cells in EAE is Npy1r, encoding Neuropeptide Y receptor 1, which scored as the top gene hit (Fig. 4A). The NPY/NPY1R axis plays a protective role in EAE by suppressing MOG35-55-reactive Th1 cells 64. Moreover, polymorphisms in the human NPY gene are associated with susceptibility to MS 65. Previous studies show the presence of Treg cells in the meninges and CNS parenchyma 66-68. Analysis revealed that at steady state NPY1R+ Treg cells were selectively enriched in the CNS (brain, Spinal cord and meninges) in mice as young as 4 weeks of age and they were maintained in 1 year old mice (Fig. 6 A; Fig. S7A). Expression of NPY1R and Notch3 on CNS Treg cells was mutually exclusive, indicating they represented distinct Treg cell populations (Fig. 6B). Whereas the number of Notch3+ Treg cells expanded considerably following EAE induction in the CNS of Notch3-sufficient mice (Foxp3YFPCre), those of NPY1R+ Treg cells were minimally affected (Fig. 6C-D). In contrast, NPY1R+ Treg cells markedly increased both in frequencies and in numbers in Foxp3YFPCreNotch3Δ/Δ mice following EAE induction (Fig. 6C).

Fig. 6. NPYR1+ Treg cells subpopulation are enriched in the CNS.

Fig. 6.

A. Flow cytometric analysis and frequencies of NPY1R+ and Notch3+ Treg cells at steady state and at day 15 after EAE in Foxp3YFPCre mice in the respective tissue. B. Flow cytometric analysis and cell frequencies of NPY1R+Notch3–, NPY1R+Notch3+ and NPY1R–Notch3+ cells among CNS Treg cells. C and D. Frequencies and cell numbers of NPY1R+ (C) and Notch3+ (D) Treg cells in either PBS or EAE (Day15) Foxp3YFPCre and Foxp3YFPCreNotch3Δ/Δ mice. E. MFI of Foxp3 and Helios in CNS NPY1R+ versus Notch3+ Treg cells at steady state. F. Frequencies and MFI of α4β7 in CNS NPY1R+ versus Notch3+ Treg cells at steady state. G. Flow cytometric analysis, cell frequencies an MFI of CD103 expression in CNS NPY1R+ versus Notch3+ Treg cells at steady state. H-K, Frequencies and MFI of CD69 (H), CD49d (I), CCR5 (J), CXCR3 (K) in CNS NPY1R+ versus Notch3+ Treg cells at steady state. L, Flow cytometric analysis and cell frequencies of CNS and NPY1R+ Treg cells in SPF mice, GF mice, or GF mice recolonized with FMT. M-O. Volcano plot (M) and IPA pathway analysis (N) and IPA upstream regulator analysis (O) of gene transcripts of CNS NPY1R+ versus Notch3+ Treg cells Treg cells after EAE induction (Day15). Panels A-F represent a pool of four experiments, panels G-K represent a pool of three experiment. For panels E-K, some of the Notch3+ Treg cells data points were also used in Figure 3. Error bars are SEM. Each point denotes one mouse. Statistical analysis: two-way ANOVA with Sidak post-hoc analysis (A and B) or one-way ANOVA with Dunnett’s post-hoc analysis (C, D, L) or student two-tailed t-test (E-K).

Further immune phenotypic analysis confirmed that NPY1R+ and Notch3+ Treg cells represented two distinct populations. NPY1R+ Treg cells differentially expressed markers of CNS-tissue residency including CD69, CD103, CCR5 and CXCR3 69, consistent with the ontogeny of the NPY1R+ Treg cells as brain tissue-resident Treg cells (Fig. 6 E-K). In contrast, Notch3+ Treg cells expressed the gut homing receptor α4β7 and had lower expression levels of Foxp3 and Helios compared to NPY1R+ Treg cells, consistent with their relative instability (Fig. 6E-K). Furthermore, and unlike Notch3+ Treg cells, the frequency of CNS NPY1R+ Treg cells were unchanged in GF mice compared to GF mice reconstituted with fecal microbiota transplant (FMT) or SPF mice (Fig. 6L). Ultra-low input bulk RNA-seq analysis of sorted NPY1R+ an Notch3+ Treg cells revealed that NPY1R+ Treg cells are metabolically distinct, being enriched in lipid and tryptophan metabolic pathways (Ankrd9, Ces2c, Mecr, Cpt2) 70-72 and having increased expression of genes involved Treg cell function (Ctnnd1, Hmox1, Ada, Ciita, Xcl1, Dgat2, Jund) 73-83 (Fig. 6M). Pathway analysis and upstream regulator analysis confirm that NPY1R+ Treg cells present changes in their metabolism whereas Notch3+ Treg present an increase in TLR/MyD88 activation (Fig. 6 N and O).

To further elucidate the role of NPY1R+ versus Notch3+ Treg cells in the pathogenesis of EAE, we analyzed the changes in these two Treg cell subsets over time following EAE induction in WT mice (Foxp3YFPCre). Notch3+ Treg cells were found expanded at day 15 and they receded at day 30 post EAE, whereas NPY1R+ Treg cells exhibited decreased frequencies at day 15 primarily due to the expansion of Notch3+ Treg cells in the face of largely unchanged NPY1R+ Treg cell numbers. The frequencies of NPY1R+ Treg cells rebounded back at day 30 post EAE in association with modestly increased cell numbers (Fig 7A, B). We further analyzed EAE induction in WT mice (Foxp3YFPCre) and in mice with either Treg cell-specific Notch3 or NPY1R deficiency (Foxp3YFPCreNotch3Δ/Δ, Foxp3YFPCreNpy1rΔ/Δ) or combined Notch3 and NPY1R deficiency (Foxp3YFPCreNotch3Δ/ΔNpy1rΔ/Δ). Foxp3YFPCreNpy1rΔ/Δ mice were asymptomatic at steady state and did not develop spontaneous disease when followed for up to one year (Fig S1A-N). Whereas mice with Treg cell-specific Notch3 deficiency were protected against EAE, mice with Treg cell-specific NPY1R deficiency exhibited severe disease post EAE induction that peaked earlier (d9 for NPY1R-deficient vs. d15 for Notch3-sufficient mice) with fatal outcome (Fig. 7C, D). Furthermore, combined Treg cell Notch3 and NPY1R deficiency partially reversed the protective effect of isolated Treg cell Notch3 deficiency (Fig. 7C, D, Fig S7 B). Whereas Notch3+ Treg cells were enriched in MOG35-55 tetramer staining, NPY1R+ Treg cells had reduced staining compared to NPY1R– Treg cells (Fig. S7C). Analysis of cytokine expression in EAE at d9 and d15 revealed that in contrast to Notch3 deficiency, which suppressed IL-17 expressing CNS Tconv cell populations at d15, Treg cell-specific NPY1R deficiency resulted in the selective expansion of IFNγ+ and IFNγ+GM-CSF+ Tconv cells, implicated in the pathogenesis of MS and EAE (Fig. 7E-H) 18,84,85. The protective role of NPYR1+ Treg cells in EAE was further supported in studies using the NPYR1 inhibitor BIBO03304 86, which largely abrogated the protective effect of Treg cell-specific Notch3-deficiency. Treatment with BIBO03304 at a high dose induced lethality and severe EAE in WT (Foxp3YFPCre) mice, consistent with results obtained with Foxp3YFPCreNpy1rΔ/Δ mice (Fig. S7D-G). Overall, these results established the presence of two distinct CNS Treg cell subpopulations in EAE: one gut-derived pathogenic population expressing Notch3 and another tissue resident protective population expressing NPY1R.

Fig. 7. Notch3 deletion promotes the expansion of a protective NPYR1+ Treg cells subpopulation.

Fig. 7.

A, B. Time course of CNS NPY1R+ versus Notch3+ Treg cell frequency (A) and number (B) changes after EAE induction. C. Time scores and cumulative and maximal scores of EAE in Foxp3YFPCre, Foxp3YFPCreNotch3Δ/Δ, Foxp3YFPCreNpyr1Δ/Δ, and Foxp3YFPCreNotch3Δ/ΔNpyr1Δ/Δ mice. D. Kaplan Meyer survival curves of the respective mouse strain in EAE. E. Cell frequencies of IFNγ and GM-CSF single- and double-positive Tconv cells of the respective mouse strains in EAE (day 9). F. Cell frequencies of IFNγ+ and IL-17+ Tconv cells of the respective mouse strain in EAE (day 9). G. Flow cytometric analysis and cell frequencies of IFNγ and GM-CSF single- and double-positive Tconv cells of the respective mouse strains in EAE (day 15). H. Cell frequencies of IFNγ and IL-17 single- and double-positive Tconv cells of the respective mouse strains in EAE (day 15). Panels A-H represent a pool of three experiments. Error bars are SEM. Each point denotes one mouse. Statistical analysis: two-way ANOVA with Sidak post-hoc analysis (A, B), one-way ANOVA with Dunnett’s post-hoc analysis (C, E-H) and Log-rank test (D).

Discussion.

In this study, we identify alterations in distinct Treg cell populations as playing a central role in the pathogenesis of autoimmune neuroinflammation relevant to MS. First, we identify a gut microbiota-regulated Treg cell population expressing Notch3 and enriched in MOG35-55-specific autoreactive TCR that translocates to the CNS to promote neuroinflammation. The identification of this population, also demonstrated in human subjects with MS, expands on previous findings on the role the microbiota in inducing autoreactive T cells in EAE and MS 49-55 by showing a crucial role of Notch3 induction in this process. Notch3+ Treg cells were found at steady state in the gut and in the CNS of microbiota-sufficient mice. They were drastically decreased in both tissues in germ-free mice but re-established upon fecal transplant of these mice. These results established that Notch3+ Treg cells represent a microbiota responsive Treg cell population in the gut that may contribute to mucosal tolerance at steady state and that this population circulates between the gut and the brain, aided by their expression of CNS homing markers including CD49d, CCR5 and CXCR3. In the context of EAE, autoreactive Notch3+ Treg cells expand, possibly driven by molecular mimicry between the self and bacterial antigens 49-55,87. The translocated Notch3+ Treg cells are destabilized upon their specific interaction with DLL1+ microglia into Th17 cells that promote neuroinflammation. These results reconcile earlier findings on the impaired function of Treg cells in MS and EAE and the expansion of pathogenic Th17 cells in the CNS by relating them to a common Notch3 Treg cell pathway 13,25.

Critically, our results indicate that the outcome of EAE is shaped by the balance of Notch3+ Treg cells with a second tissue-resident NPY1R+ Treg cell population that normally restrains IFNγ+ and IFNγ+GM-CSF+ Tconv cells. Thus, the dichotomous IL-17+ and IFNγ+GM-CSF+ CD4+ T cell responses noted in MS/EAE 10,13,18,88-90 are linked to an underlying dynamic interplay between Notch3+ Treg cells, which promote the Th17 cell response, and NPY1R+ Treg cells that control the IFNγ/GM-CSF Th cell responses. NPY1R+ Treg cells were distinguished from their Notch3+ counterparts by several molecular features associated with enhanced Treg cell function (Ctnnd1, Hmox1, Ybx-1, Jund) and components of lipid metabolic pathways (e.g. Mcat) 73-83. These results are consistent with a study documenting increased expression of lipid metabolic pathways in brain resident T cells 68. Further investigation will be required to elucidate the distinct ontogeny of the respective Treg cell populations, the mechanisms by which they interact in disease pathogenesis including different subtypes of MS and means of manipulating their responses to promote long-term immune tolerance.

In human subjects with MS the frequencies of Notch3+ Treg cells decline during remission, a phenomenon also observed in mice upon EAE resolution. This indicates that the expansion of these cells is intimately associated with disease activity. Nevertheless, disease resolution in mice is associated with persistence of a Notch3+Foxp3– IL-17-producing cell population in the CNS, suggesting that this population may be reactivated during disease relapse. The precise Treg and ex-Treg cell populations in the CNS during remitting-relapsing disease will require further investigation.

Treg cell Notch receptors play non-redundant roles in directing tissue-specific inflammation. For example, inducible Notch4 expression on lung Treg cells promotes lung inflammation in the contexts of allergy35,37, influenza and SARS-CoV2 36. In contrast, Notch1 expression in Treg cells promotes acute graft-versus-host disease and graft rejection 34,91, while the constitutive expression of the intracellular domain of Notch1 in Treg cells results in severe immune dysregulation 34. We demonstrated that increased expression of Notch1 on Treg cells plays a critical role in the evolution of the multiple inflammatory syndromes in Children (MIS-C) post SARS-CoV2 infection. 33. Collectively, these data suggest that the co-opting of Treg cell Notch receptor signaling by different triggers enables or “licenses” tissue-specific inflammatory outcomes. Notch signaling in Treg cells may thus provide critical tissue and pathogen-related cues necessary for Treg cell functional tuning. Physiologically, Notch signaling may act to modulate Treg cell programs to ensure optimal immune responses to pathogens. When misdirected, such as in the case of Notch3 in MS/EAE, it results in immune dysregulation, inflammation and autoimmunity. The concept of a Treg cell Notch code to license tissue inflammation raises questions regarding mechanisms of Notch receptor tissue specificity, receptor-ligand interaction specificity, and common and divergent downstream signaling pathways that underpin this code. Studies addressing these issues would be critical for effectively targeting these pathways for therapeutic purposes in the future.

Limitations of Study

There are limitations to this study that should be noted. The studies on human subjects with MS are cross-sectional and they do not include longitudinal follow-up of the same patients over time including during repeat cycles of remission and relapse. The control subject group recruited for the CSF T cell studies is older than the MS patient group. The mouse studies were limited to a model of acute EAE and did not extend to models of chronic remitting-relapsing disease. The physiologic functions at steady state of Notch3+ Treg cells in the gut and upon their circulation in the CNS as well as those of CNS tissue resident NPY1R+ Treg cells remain to be fully established. The results obtained in the current studies provide a foundation to address these questions in future investigations.

RESOURCE AVAILABILITY

Lead contact:

Talal A. Chatila, M.D., M.Sc., Division of Immunology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA. Email: talal.chatila@childrens.harvard.edu

Materials availability:

Any data and materials to be shared will be released via a material transfer agreement.

Data and code availability:

Bulk-RNA (GSE253097) and Sc-RNAseq (GSE253602) sequencing datasets have been deposited in the Gene Expression Omnibus. All the script have been deposited in Github (https://github.com/OlgaLanzetta/Notch3-MS)

STAR Methods:

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Human Cohorts:

Multiple sclerosis and control patients:

Peripheral blood (PB) samples were obtained from MS patients (n=10) and healthy control (n=6) from Institut du cerveau, Paris, France. Moreover, PB and cerebrospinal Fluid (CSF) cells were obtained from Ms patients (n=27), healthy control (n=14) and non-MS patients (n=13) from Italy. The clinical characteristics of MS patients and Controls groups are reported in Table S1.

Sample Processing:

Peripheral blood samples were obtained in sodium heparin or ethylenediaminetetraacetic acid (EDTA) tubes at study enrollment. At each site, peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll density gradient centrifugation and cryopreserved in liquid nitrogen.

Human Subjects:

Written informed consent (and assent when appropriate) was provided by the participants. The Institutional Review Boards (IRBs) approved the single-center research protocols.

Isolation of Human peripheral blood mononuclear cells (PBMCs).

Human PBMCs were isolated from whole blood from either healthy control or MS patients via density gradient using Ficoll (GE Healthcare). PBMCs were then stored frozen in Fetal Calf Serum (FCS) (Sigma Aldrich) and 15% Dimethyl sulfoxide (DMSO) (Sigma Aldrich). The cells were later thawed for analysis of their Notch or different markers and cytokine expression by flow cytometry.

Mice.

The following mouse strains were obtained from the JAX Laboratories: CD4Cre (B6.Cg-Tg(Cd4-cre)1Cwi/BfluJ), Foxp3YFPCre, Notch1fl/fl (B6.129X1-Notch1tm2Rko/Grid/J), Notch2fl/fl (B6.129S-Notch2tm3Grid/J), Notch3−/− (B6;129S1-Notch3tm1Grid/J), Wwtr1fl/fl (B6.129(Cg)-Wwtr1tm1Hmc/J), Yap1fl/fl (B6.129P2(Cg)-Yap1tm1.1Dupa/J), 2D2 (C57BL/6-Tg(Tcra2D2,Tcrb2D2)1Kuch/J), Tlr4fl/fl (B6(Cg)-Tlr4tm1.1Karp/J), Tlr2fl/fl (B6.Cg-Tlr2tm1.1Stplz/J), Tlr5fl/fl (B6(Cg)-Tlr5tm1.1Gewr/J), Myd88fl/fl (B6.129P2(SJL)-Myd88tm1Defr/J) and lfngtfl/fl (FVB.129-Lfngtm1.1Egan/Mmjax), Rosa26LSL-RFP (B6.Cg-Gt(ROSA)26Sortm1Hjf/J). Notch4fl/fl (Notch4tm1c(NCOM)Mfgc) were obtained from the Canadian mutant mouse repository. Notch3fl/fl (C57BL/6JGpt-Notch3em1Cflox/Gpt) and Npy1Rfl/fl (C57BL/6JGpt-Npy1rem1Cflox/Gpt) were obtained from the GemPharmatech. All mutant mouse strains and their respective crosses were backcrossed 8–10 generations on C57BL/6 background. All conditional knock-out mice were validated by RT-qPCR analysis. The mice were housed under specific pathogen-free conditions and used according to the guidelines of the Institutional Animal Research Committees at the Boston Children’s Hospital.

Antigens and EAE induction.

EAE was induced in mice by immunization with 200 μg MOG35-55, emulsified (1:1) in complete Freund’s adjuvant (Hooke laboratories, Cat # EK-2110). Each mouse received 0.2 ml emulsion subcutaneously and pertussis toxin (200 ng intravenously; Hooke laboratories) at 0 and 2 dpi. Animals were followed for the development of EAE and graded clinically on a scale of 0–4. Clinical scores were recorded daily by an experimenter blinded to the experimental groups. Scores were assigned as follows: 0: no sign of disease, 0.5: partial tail paralysis, 1: loss of tone in the tail, 2: hind limb paresis, 3: hind limb paralysis, 4: tetraplegia and moribund 92,93.

Anti-α4β7 (Bioxcell, Cat# #BE0034) and anti- VLA-4 (Bioxcell, Cat# #BE0071) mAb treatment:

Mice were immunized as previously described and then treated with either anti-α4β, anti-α4β7 mAb or an isotype control at 5 mg/kg every 2 days.

BIBO3304 Treatment:

Mice were immunized as previously described and then treated with 100μg/kg of BIBO3304 every 2 days. Controls mice were treated with PBS.

METHOD DETAILS

Isolation of lymph node and CNS leukocytes.

Lymph nodes, spinal cord, meninges and brains (CNS) were collected in complete RPMI (Gibco) supplemented with 10 % fetal calf serum (FCS). Lymph nodes were mashed in a Potter and filtered through a 70 μm cell strainer. Meninges cells were isolated as previously described 94. Brain and spinal cord were collected and were mashed in a Potter and filtered through a 70 μm cell strainer. Cells were then washed and suspended in 40% Percoll. After a 20 min centrifugation at 600 g the cells pellet was collected. In both cases, erythrocytes were lysed using ACK buffer (100 μM EDTA, 160 mM NH4Cl and 10 mM NaHCO3).

Single-cell RNA sequencing.

CNS CD4 T cells from MOG-immunized Foxp3YFPCre (n=2) and Foxp3YFPCreNotch3Δ/Δ (n=3) mice were cells sorted (Sony sorter MA900). A total of 20,000 cells were loaded per 10x Genomics chip; therefore, Cells were encapsulated, barcoded and lysed to enable the generation of cDNA libraries for transcriptome and HTO sequencing using the 10x Genomics technology (4). Libraries were sequenced on an Illumina NovaSeq 6000.

Sequencing data from each 10x run were processed with the CellRanger pipeline (10x Genomics, cloud analysis) for demultiplexing and gene alignment (4). The resulting matrices were imported in R (v. 4.3.1) and processed using the Seurat pipeline v.4.4 (5). First, we evaluated the overall quality of each sample. We filtered out low-quality cells, retaining only cells with more than 500 and less than 30000 reads, more than 200 and less than 3000 detected genes, and less than 10% of mitochondrial genes. As a further quality assessment, we also used SingleR v2.4 (6) on the raw counts to classify the cell types according to the main labels of the ImmGenData reference available in the celldex package v. 1.12. We retained for further analysis only cells classified as “T cells” or “NKT,” which constituted more than 95% of the high-quality cells. Finally, we follow the default Seurat pipeline for each sample, i.e., we log-normalized the raw counts, identified the 2000 high variable genes, scaled the gene expression, and performed dimension reduction with PCA using 30 dimensions. After these steps, we identified and removed potential doublets in each sample using DoubletFinder v.2.0.3 (7), after estimating the best set of parameters when the ground truth is unavailable. After the above-described quality assessment steps, we integrated the samples using the anchor-based canonical correlation analysis (CCA) integration approach. For this purpose, we first selected the top 20000 scoring features; then, we identified the set of anchors in the Seurat objects list containing all available samples; finally, we performed the CCA integration with the given anchors. We scaled the integrated data and performed dimension reduction with PCA with 30 components. After that, we identified the k-nearest neighbors and performed the clustering analysis with the original Louvain algorithm and resolution parameter 0.15. We annotated the clusters based on the top 20 markers across all identity classes computed with FindAllMarkers (logfc.threshold = 0.25, min.pct = 0.15). To support and refine cluster annotation, we assigned cluster identities with the assistance of ChatGPT (OpenAI, GPT-4-turbo, July 2024 version), which we used to interpret gene expression patterns in the context of known murine CD4⁺ T cell populations. We assessed the significance of cell proportion within each cluster using the propeller function from the speckle package v 1.2.0 (9). We visualized cell clustering in the Uniform Manifold Approximation and Projection (UMAP) space, and we used specific markers to assess its reliability.

Pseudobulk differential expression analyses (DEA).

We used a pseudo-bulk approach to identify cluster-specific differentially expressed (DE) genes between MOG-immunized Foxp3YFPCre and Foxp3YFPCreNotch3Δ/Δ mice. We aggregated cell raw counts for each cluster, summing up the counts of all cells in each sample, thus resulting in cluster-specific raw count matrices with all 5 sequenced samples. For each cluster, we retained only those with at least 10 reads as expressed genes and identified DE genes using the DESeq2 pipeline with default parameters (10), choosing FDR=0.05 as the significance cut-off. We performed pathways and upstream regulators analysis using ingenuity pathway analysis (IPA, Qiagen) with a p-value<0.05 and a log(fold change) >1.

Flow cytometric analysis of mouse and human cells.

Antibodies against the following murine antigens were used for flow cytometric analyses: Foxp3 (FJK-16S, catalogue no: 48–5773-82 1:300, Thermofisher), IFNγ (XMG1.2, catalogue no: 505825 1:300, Biolegend), Helios (22F6, catalogue no: 47–9883-42 1:200, Thermofisher, CD4 (GK1.5, catalogue no: 100451, 1:500, Biolegend), IL-17 (TC11–18H10.1, catalogue no: 506922, 1:200, Biolegend), CD45 (30-F11, catalogue no: 103140, 1:300, Biolegend), Notch4 (HMN4–14, catalogue no: 128407 1:400, Biolegend), α4β7 ( DATK32, Catalogue no: 120606, 1:300 Biolegend), Notch1 (HMN1–12, Catalogue no: 130607, 1:500, Biolegend), Notch2 (HMN2–35, Catalogue no: 130713, 1:500, Biolegend), Notch3 (HMN3–133, Catalogue no: 130511, 1:300, Biolegend), IL-22 (Poly5164, Catalogue no: 516406 1:200, Biolegend), GM-CSF (MP1–22E9, Catalogue no: 505405, 1:200, Biolegend), Yap (D8H1X, Catalogue no: 14729S, 1:200, CST), DLL1 (HMD1–3, Catalogue no: 128307, 1:200, Biolegend), DLL4 (HMD4–1, Catalogue no: 130813, 1:200, Biolegend), Jag1 (HMJ1–29, Catalogue no: 130907, 1:200, Biolegend), Jag2 (FAB4748G, Catalogue no: FAB4748G-100UG, 1:200, R&D), Npy1R (sc-393192, Catalogue no: sc-393192 AF647, 1:200, Scbt), CD206 (C068C2, Catalogue no: 141719, 1:300, Biolegend), AhR (4MEJJ, Catalogue no: 50–5925-82, 1:200, Invitrogen), CD86 (A17199A, Catalogue no: 159203, 1:300, Biolegend), CD69 (H1.2F3, Catalogue no: 104511, 1:300, Biolegend), CD103 (2E7, Catalogue no: 121425, 1:300, Biolegend), CCR5 (HM-CCR5, Catalogue no: 107015, 1:300, Biolegend), CD49d (R1–2, Catalogue no: 103617, 1:300, Biolegend), CXCR3 (S18001A, Catalogue no: 155911, 1:300, Biolegend), CD8 (QA17A07, Catalogue no: 155015, 1:300, Biolegend), CD44 (IM7, Catalogue no: 103035, 1:300, Biolegend), CD62L (MEL-14, Catalogue no: 104411, 1:300, Biolegend), CD11b (M1/70, Catalogue no: 101205, 1:300, Biolegend), CD11c (N418, Catalogue no: 117321, 1:300, Biolegend), I-Ab/d (AF6–120.1, Catalogue no: 116417, 1:300, Biolegend).

Antibodies against the following human antigens were used: CD3 (HIT3a, catalogue no: 300318, 1:200,Biolegend), CD4 (RPA-T4, catalogue no: 300530, 1:200,Biolegend), Foxp3 (PCH-101,catalogue no: 48–4776-42, 56–4716-41 1:100 Thermofisher), Notch1 (HMN1–519, catalogue no: 566023, 1:300, BD Pharmingen), Notch2 (HMN2–25, catalogue no: 742291, 1:300, BD Pharmingen), Notch3 (HMN3–21, catalogue no: 744828, 1:300, BD Pharmingen), Notch4 (HMN4–2, Catalogue no: 563269, 1:300, BD Pharmingen), CD127 (A019D5, Catalogue no: 351320 1:300, Biolegend), IL-22 (2G12A41, Catalogue No: 366710, Biolegend), IFNγ (45.83, Catalogue no: 502546, Biolegend), IL-17 (BL168, Catalogue no: 512338, Biolegend). The specificity and optimal dilution of each antibody was validated by testing on appropriate negative and positive controls or otherwise provided on the manufacturer’s website. Intracellular cytokine staining was performed as previously described 33,95. Dead cells were routinely excluded from the analysis based on the staining of eFluor 780 Fixable Viability Dye (1:1000 dilution) (Thermofisher). For MOG35-55 Tetramer staining, Cells from the CNS were incubated for 2 h at room temperature with I-Ab MOG38–49 Tetramer-PE (NIH tetramer core facility) and then stained for surface and intracellular markers. Stained cells were analyzed on a BD LSR Fortessa cell analyzer (BD Biosciences) or a CYTOFLEX LX cell analyzer (Beckman Coulter) and data were processed using Flowjo (Tree Star Inc.).

Transcriptome Profiling.

Treg cells or Microglia (CD45int CD11b+) were isolated at the peak of the disease from either MOG35-55-immunized Foxp3YFPCre or MOG35-55-immunized Foxp3YFPCreNotch3Δ/Δ mice by cell sorting (Sony Sorter MA900). mRNA was isolated using Qiagen RNeasy mini kit (Qiagen). RNA was then converted into double-stranded DNA (dsDNA), using SMART-Seq v4 Ultra Low Input RNA kit (Clontech). dsDNA was then fragmented to 200–300 bp size, using M220 Focused-ultrasonicator (Covaris), and utilized for construction of libraries for Illumina sequencing using KAPA Hyper Prep Kit (Kapa Biosystems). Libraries were then quantified using Qubit dsDNA HS (High Sensitivity) Assay Kit on Agilent High Sensitivity DNA Bioanalyzer.

Gene-level read counts were quantified using feature Counts and the latest Ensembl mouse annotation (GRCm38.R101). Raw data were trimmed using Trimmomatic (version 0.39, default parameters), tool for Illumina NGS data. To identify differentially expressed genes, we used 3 algorithms: DESeq2 (version 1.26.0), edgeR (version 3.28.1) and Lima (3.42.2) Bioconductor packages with default parameters. Count tables were normalized to TPM (Transcripts per Million) for visualizations and QC. Sample clustering, path analyses and integration of the results were performed using a custom-made pipeline available upon request (Variant Explorer RNAseq). Transcripts were called as differentially expressed when the adjusted p values were below 0.05, fold-changes over ±1.5 and false discovery rate (FDR) were below 0.05. For our path analyses, we tested 10,715 biological pathways from KEGG and GO annotations. We filtered the results using an adjusted p-value below 0.001.

In vitro co-culture experiments.

Cell-sorted CNS Treg (2.105 ) cells from either MOG35-55-immunized Foxp3YFPCre, Foxp3YFPCreNotch3Δ/Δ, Foxp3YFPCreYapΔ/ΔWwTr1Δ/Δ or Foxp3YFPCrelfngtΔ/Δ, mice were co-cultivated in the presence of Cell-sorted microglia (2.105) from MOG35-55-immunized Foxp3YFPCre and stimulated at 37°C 5% CO2 for 3 days with 100 μg/ml of MOG35-55 peptide in 96-well, round-bottomed plates. The Treg destabilization and microglia polarization were analyzed by flow cytometry. These experiments were also performed in the presence of anti-Jag2 (catalogue no: 131004, 1μg/ml, Biolegend), anti-DLL1 (catalogue no: HMD1–3, 1μg/ml, Biolegend), or isotype control (catalogue no: HTK888, 1μg/ml, Biolegend).

Treg cells from Foxp3YFPCre (2.105) were also co-cultivated in the presence of Cell-sorted microglia (2.105) from MOG35-55-immunized and stimulated at 37°C 5% CO2 for 2 days with 0,10.100 μg/ml of MOG35-55 peptide in 96-well, round-bottomed plates. Brefeldin A was added for the last 5h. Cells were then stained with different antibodies and analyzed by flow cytometry.

QUANTIFICATION AND STATISTICAL ANALYSIS

Student’s two-tailed t-test, Log-rank test, one- and two-way ANOVA and repeat measures two-way ANOVA with post-test analysis.

Supplementary Material

MMC2
MMC1

KEY RESOURCES TABLE

REAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
eBioscience Fixable Viability Dye eFluor 506 and eFluor 780 Thermo Fischer Cat# 65–0866-18
APC-Cy7 and FITC anti-mouse CD3 Antibody Biolegend Cat# 100222
Cat# 100204
Brilliant Violet 605™ anti-mouse CD4 Antibody Biolegend Cat# 100451
Integrin alpha 4 beta 7 (LPAM-1) Monoclonal Antibody, PerCP-eFluor™ 710, eBioscience Cat# 46–5887–82
Brilliant Violet 605™ anti-mouse CD86 Antibody Biolegend Cat# 105036
PE-Anti-mouse Notch 1 Antibody Biolegend Cat# 130607
FOXP3 Monoclonal Antibody (FJK-16s), eFluor 450, eBioscience™ Thermo Fischer Cat# 48–5773–82
PE/Cyanine7 anti-mouse IFN-γ Antibody Biolegend Cat# 505825
APC anti-mouse Notch 2 Antibody Biolegend Cat# 130713
Alexa Fluor® 647 anti-mouse Notch 3 Antibody Biolegend Cat# 130511
Alexa Fluor® 700 anti-mouse IL-17A Antibody Biolegend Cat# 506914
PerCP/Cyanine5.5 anti-mouse IL-22 Antibody Biolegend Cat# 516411
PE anti-mouse GM-CSF Antibody Biolegend Cat# 505405
PE anti-mouse DLL1 Antibody Biolegend Cat# 128307
APC anti-mouse DLL4 Antibody Biolegend Cat# 130813
PE anti-mouse CD339 (Jagged 1) Antibody Biolegend Cat# 130907
Mouse Jagged 2 Alexa Fluor® 488-conjugated Antibody R&D Cat# FAB4748G-100UG
NPY1-R Antibody (E-4) Alexa Fluor® 647 Scbt Cat# sc-393192 AF647
PE/Cyanine7 anti-mouse CD206 (MMR) Antibody Biolegend Cat# 141719
AHR Monoclonal Antibody (4MEJJ), eFluor™ 660 Invitrogen Cat# 50–5925–82
PE anti-mouse CD86 Antibody Biolegend Cat# 159203
PE/Cyanine7 anti-mouse CD69 Antibody Biolegend Cat# 104511
PE/Cyanine7 anti-mouse CD103 Antibody Biolegend Cat# 121425
PerCP/Cyanine5.5 anti-mouse CD195 (CCR5) Antibody Biolegend Cat# 107015
PE/Cyanine7 anti-mouse CD49d Antibody Biolegend Cat# 103617
PerCP/Cyanine5.5 anti-mouse CD183 (CXCR3) Antibody Biolegend Cat# 155911
APC/Cyanine7 anti-mouse CD8a Recombinant Antibody Biolegend Cat# 155015
PerCP/Cyanine5.5 anti-mouse/human CD44 Antibody Biolegend Cat# 103031
APC anti-mouse CD62L Antibody Biolegend Cat# 104411
FITC anti-mouse/human CD11b Antibody Biolegend Cat# 101205
Pacific Blue™ anti-mouse CD11c Antibody Biolegend Cat#117321
APC anti-mouse I-Ab Antibody Biolegend Cat# 116417
APC/Cyanine7 anti-human CD3 Antibody Biolegend Cat# 300318
PerCP/Cyanine5.5 anti-human CD4 Antibody Biolegend Cat# 300530
FOXP3 Monoclonal Antibody (PCH101), eFluor™ 450 Thermofisher Cat# 48–4776–42
BB515 Mouse Anti-Human Notch1 BD Biosciences Cat# 566023
BV605 Mouse Anti-Human Notch2 BD Biosciences Cat# 742291
BV421 Mouse Anti-Human Notch3 BD Biosciences Cat# 744828
PE Mouse Anti-Human Notch4 BD Biosciences Cat# 563269
PerCP/Cyanine5.5 anti-human IL-22 Antibody Biolegend Cat# 366710
PE/Dazzle™ 594 anti-human IFN-γ Antibody Biolegend Cat# 502546
Brilliant Violet 785™ anti-human IL-17A Antibody Biolegend Cat# 512338
MOG38–49 Tetramer NIH Facility
CLIP-Tetramer NIH Facility
HELIOS Monoclonal Antibody (22F6), APC-eFluor 780, eBioscience™ Thermo Fischer Cat# 47–9883–42
PE/Cyanine7 anti-mouse CD11c Antibody Biolegend Cat# 117317
FITC anti-mouse I-Ad Antibody Biolegend Cat# 115006
Brilliant Violet 605™ anti-mouse CD45 Antibody Biolegend Cat# 103140
PerCP/Cyanine5.5 anti-mouse/human CD11b Antibody Biolegend Cat# 101227
Brilliant Violet 605™ anti-mouse CD4 Antibody Biolegend Cat# 100451
APC anti-mouse CD62L Antibody Thermo Fischer Cat# 17–0621–82
Brilliant Violet 605™ anti-mouse CD45 Antibody Biolegend Cat# 103139
PE anti-mouse Notch 4 Antibody Biolegend Cat# 128407
YAP (D8H1X) XP® Rabbit mAb (Alexa Fluor® 488 Conjugate) CST Cat# 14729S
PE/Cyanine7 anti-human CD127 (IL-7Rα) Antibody Biolegend Cat# 351320
APC/Cyanine7 anti-human CD3 Antibody Biolegend Cat# 300317
PerCP-Cyanine5.5 anti-human CD4 Antibody Thermo Fischer Cat# 45–0049–42
PE-Cy™7 Mouse Anti-Human IFN-γ BD Biosciences Cat# 560741
InVivoMAb anti-mouse-α4β7 BioxCell Cat# BE0034
InVivoMAb anti-mouse-VLA-4 BioxCell Cat# BE0071
PE anti-human Jagged 2 Antibody Biolegend Cat# 131004
APC anti-mouse DLL1 Antibody Biolegend Cat# 128314
Purified Armenian Hamster IgG Isotype Ctrl Antibody Biolegend Cat# 400901
InVivoMAb rat IgG2a isotype control BioxCell Cat# BE0089
InVivoMAb rat IgG2b isotype control BioxCell Cat# BE0090
Critical Commercial Assays
EAE Kit Hooke lab Cat# EK-2110
BIBO3304 MCE Cat# HY-107725
Dynabeads™ Mouse T-Activator CD3/CD28 Thermo Fischer Cat# 11452D
MOG peptide (35–55) Covalab Cat# 00110288
Chromium™ Next GEM Single Cell 3’ Kit v3.1 10x Genomics Cat# 1000268
Library Construction Kit 10x Genomics Cat# 1000190
Dual Index Kit TT Set A 10x Genomics Cat# 1000215
Chromium Next GEM Chip G Single Cell Kit 10x Genomics Cat# 1000127
Experimental Models: Organisms/Strains
B6.Cg-Tg(Cd4-cre)1Cwi/BfluJ Jax Laboratory Stock No: 022071
B6.129X1-Notch1tm2Rko/Grid/J Jax Laboratory Stock No: 007181
Notch2fl/fl (B6.129S-Notch2tm3Grid/J) Jax Laboratory Stock No: 010525
B6;129S1-Notch3tm1Grid/J Jax Laboratory Stock No: 010547
Wwtr1fl/fl (B6.129(Cg)-Wwtr1tm1Hmc/J) Jax Laboratory Stock No: 032669
Yap1fl/fl (B6.129P2(Cg)-Yap1tm1.1Dupa/J) Jax Laboratory Stock No: 032192
C57BL/6-Tg(Tcra2D2,Tcrb2D2)1Kuch/J Jax Laboratory Stock No: 006912
B6(Cg)-Tlr4tm1.1Karp/J) Jax Laboratory Stock No: 024872
B6.Cg-Tlr2tm1.1Stplz/J Jax Laboratory Stock No: 037092
B6(Cg)-Tlr5tm1.1Gewr/J Jax Laboratory Stock No: 028599
B6.129P2(SJL)-Myd88tm1Defr/J Jax Laboratory Stock No: 008888
FVB.129-Lfngtm1.1Egan/Mmjax Jax Laboratory Stock No: 037160-JAX
B6.Cg-Gt(ROSA)26Sortm1Hjf/J Jax Laboratory Stock No: 038164
Notch4tm1c(NCOM)Mfgc Canadian mutant mouse repository
C57BL/6JGpt-Notch3em1Cflox/Gpt GemPharmatech Stock No: T007271
C57BL/6JGpt-Npy1rem1Cflox/Gpt GemPharmatech Stock No: T052015
Foxp3 YFPCre Jax Stock No: 016959
Deposited Data
GEO Bulk-RNA-seq: GSE253097: Notch3 Directs a Regulatory T Cells-Microglia axis to Promote Autoimmune Neuroinflammation
Single-Cell RNA-seq: GSE253602: Notch3+ Regulatory T cells Drive Autoimmune Neuroinflammation in Multiple Sclerosis
GitHub: https://github.com/OlgaLanzetta/Notch3-MS
Software and Algorithms
GraphPad Prism 10 GraphPad Software N/A
FlowJo 10.10.0 Tree Star https://www.flowjo.com/solutions/flowjo/downloads

Highlights.

  1. Notch3+ Treg cells are induced in the gut in a microbiota and TLR-dependent manner

  2. They translocate to the CNS in EAE by an integrin-dependent mechanism

  3. They degenerate in the CNS into Th17 cells by Hippo pathway and DLL1 dependent mechanisms

  4. They outcompete NPY1R+ CNS resident Treg cells that control IFNγ and GM-CSF responses

Acknowledgment

We would like to acknowledge the patients and families who participated by contributing samples for these studies. We thank Dr. Louis-Marie Charbonnier for helping design the graphical abstract. This work was supported by National Institutes of Health (NIH) grants R01 AI115699, R01 AI065617, and R01 AI06561720S1 to T.A.C., a grant from ICM under the program “Big Brain Theory” to C. L and V. Z, “Investissements d’Avenir” ANR-10-IAIHU-06, “Translational Research Infrastructure for Biotherapies in Neurosciences” ANR-11-INBS-0011-NeurATRIS to V.Z, the PRIN 2022 PNRR project P2022BLN38 - Computational approaches for the integration of multi-omics data to C.A, J.M.L.C. was supported by a Dermatology Foundation Research Career Development Award. F.B.O. was supported by an Istanbul University BAP Project number: TSA-2022-39088 and by a scholarship from TUBITAK application number 2214-a 1059B142200930. M.B was supported by a grant from the Office of Faculty Development at Boston Children’s Hospital.

Footnotes

Declaration of Interest: The authors declare no conflict of interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

MMC2
MMC1

Data Availability Statement

Bulk-RNA (GSE253097) and Sc-RNAseq (GSE253602) sequencing datasets have been deposited in the Gene Expression Omnibus. All the script have been deposited in Github (https://github.com/OlgaLanzetta/Notch3-MS)

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